「casting」の検索結果

Superforecasting with calibrated reasoning, Brier score tracking, and prediction ledger management

日本語の概要は準備中です。原文の説明を表示しています。

Build a forecasting model for a time series — demand, revenue, or usage prediction. Use when asked to "forecast demand", "predict next quarter revenue", or "build a time series model".

日本語の概要は準備中です。原文の説明を表示しています。

Perform capacity planning using historical metrics and growth models. Use predict_linear for forecasting, identify resource constraints, calculate headroom, and recommend scaling actions before saturation. Use before seasonal traffic spikes or product launches, during quarterly capacity reviews, when resource utilization trends upward, or before budget planning cycles.

日本語の概要は準備中です。原文の説明を表示しています。

Forecast infrastructure and application metrics using Prophet or statsmodels for capacity planning, cost optimization, and proactive scaling. Visualize predictions in Grafana and set up alerts for projected resource exhaustion. Use when forecasting infrastructure capacity needs for CPU, memory, or disk, planning hardware procurement for next quarter, predicting cost trends to optimize cloud spending, or setting up proactive scaling policies based on predicted load.

日本語の概要は準備中です。原文の説明を表示しています。

"Combine multiple forecasting models into ensemble predictions for improved accuracy. Use this skill when the user needs to improve forecast reliability, combine ARIMA/Prophet/ETS outputs, or build a robust forecasting pipeline — even if they say 'combine forecasts', 'model averaging', or 'which forecast should I trust'.".

日本語の概要は準備中です。原文の説明を表示しています。

"Apply exponential smoothing methods for time series forecasting with weighted moving averages. Use this skill when the user needs simple, robust forecasts, implement Holt-Winters for seasonal data, or build lightweight forecasting without complex models — even if they say 'simple forecast', 'moving average prediction', or 'smoothing method'.".

日本語の概要は準備中です。原文の説明を表示しています。

"Build forecasting models with Meta's Prophet for business time series with holidays and changepoints. Use this skill when the user needs user-friendly time series forecasting, handling of missing data and holidays, or automatic changepoint detection — even if they say 'forecast with Prophet', 'business forecast', or 'easy time series model'.".

日本語の概要は準備中です。原文の説明を表示しています。

Configuring Salesforce opportunity management: sales stages, sales processes, opportunity record types, Path configuration, opportunity teams, opportunity splits, and forecasting categories. Use when setting up or restructuring the opportunity lifecycle for Sales Cloud. NOT for forecast types, rollup methods, quotas or manager adjustments — use admin/collaborative-forecasts. NOT for agreeing what the stages and exit criteria should be before any build — use admin/sales-process-mapping. NOT for CPQ pricing or product configuration. Keywords: BusinessProcess, sales process, OpportunityStage, ForecastCategoryName, PathAssistant, OpportunitySplit, OpportunitySplitType, OpportunityTeamMember, Opportunity.settings, enableOpportunityTeam, IsClosed, IsWon, IsPrivate.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze material forecasting and requirements planning systems including MRP/MRP II logic evaluation, BOM explosion accuracy, supplier lead time variability tracking, seasonal demand pattern detection, fabric yield and marker efficiency optimization, MOQ aggregation strategies.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze demand forecasting systems — time-series decomposition (ARIMA, Prophet, ETS), seasonal and event-driven demand modeling, booking curve and pickup analysis, cancellation prediction, forecast accuracy metrics (MAPE, bias), and model retraining pipelines.

日本語の概要は準備中です。原文の説明を表示しています。

Expert-level manufacturing processes covering machining, casting, forming, welding, additive manufacturing, tolerances, and process selection.

日本語の概要は準備中です。原文の説明を表示しています。

サプライチェーン最適化コンサルティングスキル。需要予測、在庫最適化、調達戦略、 物流ネットワーク設計、S&OP(Sales and Operations Planning)を支援。 Use when optimizing supply chain operations, improving inventory management, designing logistics networks, or conducting supply chain risk assessments. Triggers: "supply chain", "在庫最適化", "inventory optimization", "demand forecasting", "S&OP", "procurement strategy", "logistics network".

Expert en budgétisation et prévisions (FP&A, variance analysis, rolling forecasts, DZ)

日本語の概要は準備中です。原文の説明を表示しています。

Real-time WebSocket features with Action Cable in Rails. Use when: (1) Building real-time chat, (2) Live notifications/presence, (3) Broadcasting model updates, (4) WebSocket authorization. Trigger keywords: Action Cable, WebSocket, real-time, channels, broadcasting, stream, subscriptions, presence, cable

日本語の概要は準備中です。原文の説明を表示しています。

Automate Castingwords tasks via Rube MCP (Composio). Always search tools first for current schemas.

日本語の概要は準備中です。原文の説明を表示しています。

Complete B2B sales system — from ICP to closed-won. Discovery frameworks, objection handling, pipeline management, forecasting, and deal acceleration. Works for any B2B company.

日本語の概要は準備中です。原文の説明を表示しています。

Weekly pipeline health analysis with velocity metrics, stage distribution, forecasting, and actionable recommendations

日本語の概要は準備中です。原文の説明を表示しています。

Retrieve probabilistic forecasts and their scorecard context. Use when the user asks what World Monitor is forecasting, how probabilities shifted, or how calibrated the forecasts are.

日本語の概要は準備中です。原文の説明を表示しています。

Three.js interaction - raycasting, controls, mouse/touch input, object selection. Use when handling user input, implementing click detection, adding camera controls, or creating interactive 3D experiences.

日本語の概要は準備中です。原文の説明を表示しています。

Fits and diagnoses Python statistical models including OLS, GLM, discrete and mixed models, ARIMA and SARIMAX. Supports coefficient inference, marginal effects, model comparison and time series forecasting with explicit design and uncertainty checks. Used for econometrics and statistical modeling; for guided test selection with APA reporting, see statistical-analysis.

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

日本語の概要は準備中です。原文の説明を表示しています。

Token unlock schedule analysis and project treasury tracking — vesting cliffs, linear unlocks, team/investor/ecosystem token releases, treasury diversification, and sell pressure forecasting.

日本語の概要は準備中です。原文の説明を表示しています。

Global macro analysis framework (central bank policy transmission / FX forecasting / geopolitical risk / capital flows), used to build macro factor signals that drive cross-asset allocation.

日本語の概要は準備中です。原文の説明を表示しています。

Plan resource capacity — workload analysis and utilization forecasting. Use when heading into quarterly planning, the team feels overallocated and you need the numbers, deciding whether to hire or deprioritize, or stress-testing whether upcoming projects fit the people you have.

日本語の概要は準備中です。原文の説明を表示しています。